{"id":{"repo_id":"cambridge","oai_identifier":"oai:www.repository.cam.ac.uk:1810/375708"},"canonical_url":"https://search.dev.ndltd.org/etd/cambridge/oai:www.repository.cam.ac.uk:1810/375708","repository":{"repo_id":"cambridge","name":"Cambridge University","base_url":"https://api.repository.cam.ac.uk/server/oai/request"},"display":{"title":"Multimodal Soft Sensor Design through Single-Material Information Structuring","abstract":"Human skin provides flexibility, compliance, pressure sensing, temperature sensing, and stimulus localisation while also being able to detect and heal damages. As technologies move towards the design of increasingly general-purpose robots, we require soft sensors which replicate these essential properties without sacrificing robustness, longevity, or straightforward fabrication. This thesis proposes a design framework in which multimodal soft sensors are considered as information-generating systems, the outputs of which can be structured through material, morphology, and electrical excitation co-design. The effects of each are experimentally explored, with the ultimate goal of single-material soft multimodal sensor fabrication. Their outputs are analysed through the learning-based processing of large physically-collected datasets, using architectures capable of adapting to the nonlinear responses of functional materials such as self-healing polymers. In doing so, numerous novel contributions are demonstrated: designing at the material level yields a 3D-printable sensorised hydrogel which responds to various multimodal stimuli; morphology explorations lead to a temperature & strain-sensitive membrane inspired by the distribution of receptors in the human skin; and multiplexed electrical excitations enable the sensorisation of a full-size 3D hand using electrodes positioned around its wrist. The thesis's experimental work culminates in the demonstration of a single-material multimodal soft skin, which holds great potential for the implementation of soft robotic sensors: the final chapter explores the vast future directions expected to stem from this work.","abstract_html":"Human skin provides flexibility, compliance, pressure sensing, temperature sensing, and stimulus localisation while also being able to detect and heal damages. As technologies move towards the design of increasingly general-purpose robots, we require soft sensors which replicate these essential properties without sacrificing robustness, longevity, or straightforward fabrication. This thesis proposes a design framework in which multimodal soft sensors are considered as information-generating systems, the outputs of which can be structured through material, morphology, and electrical excitation co-design. The effects of each are experimentally explored, with the ultimate goal of single-material soft multimodal sensor fabrication. Their outputs are analysed through the learning-based processing of large physically-collected datasets, using architectures capable of adapting to the nonlinear responses of functional materials such as self-healing polymers. 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